Description Usage Arguments Details Value Note Author(s) Examples
Computes the Weight of Evidence and Information Value between Dependent and Independent variable.
1 | woe(Data, Independent, Continuous, Dependent, C_Bin, Bad, Good)
|
Data |
: Name of Data Set |
Independent |
: Name of the Independent Variable |
Continuous |
: True if the variable is continuous, False if variable is Ordinal or Nominal |
Dependent |
: Name of the Targer Variable |
C_Bin |
: Count of Bins to be computed |
Bad |
: Which categorical variable do you want to be bad |
Good |
: Which categorical variable do you want to be good |
WOE
Returns a DataSet with computed WoE and IV values on success or 0 on Failure
"woe" shows the log-odds ratio between between Goods and Bads. In the Bivalued Dependenet variable, one value represents Goods and others are bads. In Detail with an Example: Let Dependent varaible be ATTRITED (0,1) and Independent variable be TENURE where, 1-Attrited, 0-Non Attrited. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in getting attrited, Then good would be 1 and bad=0. If I wish to check WOE and IV of Tenure with ATTRITED to know if Tenure has an effect in not getting attrited, Then good would be 0 and bad=1.
Sudarson Mothilal Thoppay
1 2 |
BIN BAD GOOD TOTAL BAD% GOOD% TOTAL% WOE IV BAD_SPLIT GOOD_SPLIT
1 4 3 8 11 0.158 0.615 0.344 135.9 0.621 0.273 0.727
2 6 4 3 7 0.211 0.231 0.219 9.1 0.002 0.571 0.429
3 8 12 2 14 0.632 0.154 0.438 -141.2 0.675 0.857 0.143
BIN MIN MAX BAD GOOD TOTAL BAD% GOOD% TOTAL% WOE IV BAD_SPLIT
1 1 10.4 14.3 4 0 4 0.211 0.000 0.125 -Inf Inf 1.00
2 2 14.7 15.2 3 1 4 0.158 0.077 0.125 -71.9 0.058 0.75
3 3 15.5 17.3 3 1 4 0.158 0.077 0.125 -71.9 0.058 0.75
4 4 17.8 19.2 4 0 4 0.211 0.000 0.125 -Inf Inf 1.00
5 5 19.2 21.0 1 3 4 0.053 0.231 0.125 147.2 0.262 0.25
6 6 21.4 22.8 2 2 4 0.105 0.154 0.125 38.3 0.019 0.50
7 7 22.8 27.3 2 2 4 0.105 0.154 0.125 38.3 0.019 0.50
8 8 30.4 33.9 0 4 4 0.000 0.308 0.125 Inf Inf 0.00
GOOD_SPLIT
1 0.00
2 0.25
3 0.25
4 0.00
5 0.75
6 0.50
7 0.50
8 1.00
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